






'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    BertTokenizerFast,
						    BlipImageProcessor,
						    GPTaTokenizer,
						    InstructBlipProcessor,
						    PreTrainedTokenizerFast,
						)


@require_vision
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Optional[Any]      )    ->							Tuple:
								UpperCAmelCase_      :       Optional[Any]          =       tempfile.mkdtemp()

								UpperCAmelCase_      :       Optional[int]          =       BlipImageProcessor()
								UpperCAmelCase_      :       List[Any]          =       GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''      )
								UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

								UpperCAmelCase_      :       Optional[int]          =       InstructBlipProcessor(_A				, _A				, _A      )

								processor.save_pretrained(self.tmpdirname      )




					def 		A      (  self  :		Union[str, Any]				, **_A  :		Dict      )    ->							Dict:
								return AutoProcessor.from_pretrained(self.tmpdirname				, **_A      ).tokenizer




					def 		A      (  self  :		Dict				, **_A  :		Any      )    ->							int:
								return AutoProcessor.from_pretrained(self.tmpdirname				, **_A      ).image_processor




					def 		A      (  self  :		str				, **_A  :		Optional[Any]      )    ->							List[Any]:
								return AutoProcessor.from_pretrained(self.tmpdirname				, **_A      ).qformer_tokenizer




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								shutil.rmtree(self.tmpdirname      )




					def 		A      (  self  :		Optional[int]      )    ->							str:
								UpperCAmelCase_      :       str          =       [np.random.randint(2_55				, size=(3, 30, 4_00)				, dtype=np.uinta      )]

								UpperCAmelCase_      :       Optional[int]          =       [Image.fromarray(np.moveaxis(_A				, 0				, -1      )      ) for x in image_inputs]

								return image_inputs




					def 		A      (  self  :		Dict      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[Any]          =       InstructBlipProcessor(
								    tokenizer=self.get_tokenizer()				, image_processor=self.get_image_processor()				, qformer_tokenizer=self.get_qformer_tokenizer()				, )
								processor.save_pretrained(self.tmpdirname      )

								UpperCAmelCase_      :       Dict          =       self.get_tokenizer(bos_token='''(BOS)'''				, eos_token='''(EOS)'''      )
								UpperCAmelCase_      :       List[Any]          =       self.get_image_processor(do_normalize=_A				, padding_value=1.0      )

								UpperCAmelCase_      :       Tuple          =       InstructBlipProcessor.from_pretrained(
								    self.tmpdirname				, bos_token='''(BOS)'''				, eos_token='''(EOS)'''				, do_normalize=_A				, padding_value=1.0      )

								self.assertEqual(processor.tokenizer.get_vocab()				, tokenizer_add_kwargs.get_vocab()      )
								self.assertIsInstance(processor.tokenizer				, _A      )

								self.assertEqual(processor.image_processor.to_json_string()				, image_processor_add_kwargs.to_json_string()      )
								self.assertIsInstance(processor.image_processor				, _A      )
								self.assertIsInstance(processor.qformer_tokenizer				, _A      )




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       self.get_image_processor()
								UpperCAmelCase_      :       Optional[int]          =       self.get_tokenizer()
								UpperCAmelCase_      :       List[str]          =       self.get_qformer_tokenizer()

								UpperCAmelCase_      :       Optional[Any]          =       InstructBlipProcessor(
								    tokenizer=_A				, image_processor=_A				, qformer_tokenizer=_A      )

								UpperCAmelCase_      :       Any          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       Optional[int]          =       image_processor(_A				, return_tensors='''np'''      )
								UpperCAmelCase_      :       Tuple          =       processor(images=_A				, return_tensors='''np'''      )

								for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()				, input_processor[key].sum()				, delta=1e-2      )




					def 		A      (  self  :		List[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       int          =       self.get_image_processor()
								UpperCAmelCase_      :       Optional[Any]          =       self.get_tokenizer()
								UpperCAmelCase_      :       List[str]          =       self.get_qformer_tokenizer()

								UpperCAmelCase_      :       Union[str, Any]          =       InstructBlipProcessor(
								    tokenizer=_A				, image_processor=_A				, qformer_tokenizer=_A      )

								UpperCAmelCase_      :       List[Any]          =       '''lower newer'''

								UpperCAmelCase_      :       int          =       processor(text=_A      )

								UpperCAmelCase_      :       Optional[Any]          =       tokenizer(_A				, return_token_type_ids=_A      )
								UpperCAmelCase_      :       str          =       qformer_tokenizer(_A				, return_token_type_ids=_A      )

								for key in encoded_tokens.keys():
											self.assertListEqual(encoded_tokens[key]				, encoded_processor[key]      )

								for key in encoded_tokens_qformer.keys():
											self.assertListEqual(encoded_tokens_qformer[key]				, encoded_processor['''qformer_''' + key]      )




					def 		A      (  self  :		str      )    ->							Tuple:
								UpperCAmelCase_      :       List[str]          =       self.get_image_processor()
								UpperCAmelCase_      :       List[Any]          =       self.get_tokenizer()
								UpperCAmelCase_      :       str          =       self.get_qformer_tokenizer()

								UpperCAmelCase_      :       Optional[Any]          =       InstructBlipProcessor(
								    tokenizer=_A				, image_processor=_A				, qformer_tokenizer=_A      )

								UpperCAmelCase_      :       Dict          =       '''lower newer'''
								UpperCAmelCase_      :       List[Any]          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       Union[str, Any]          =       processor(text=_A				, images=_A      )

								self.assertListEqual(
								    list(inputs.keys()      )				, ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']				, )

								# test if it raises when no input is passed
								with pytest.raises(_A      ):
											processor()




					def 		A      (  self  :		Optional[Any]      )    ->							str:
								UpperCAmelCase_      :       Union[str, Any]          =       self.get_image_processor()
								UpperCAmelCase_      :       List[Any]          =       self.get_tokenizer()
								UpperCAmelCase_      :       Union[str, Any]          =       self.get_qformer_tokenizer()

								UpperCAmelCase_      :       List[str]          =       InstructBlipProcessor(
								    tokenizer=_A				, image_processor=_A				, qformer_tokenizer=_A      )

								UpperCAmelCase_      :       str          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCAmelCase_      :       Optional[Any]          =       processor.batch_decode(_A      )
								UpperCAmelCase_      :       Tuple          =       tokenizer.batch_decode(_A      )

								self.assertListEqual(_A				, _A      )





					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.get_image_processor()
								UpperCAmelCase_      :       List[Any]          =       self.get_tokenizer()
								UpperCAmelCase_      :       Tuple          =       self.get_qformer_tokenizer()

								UpperCAmelCase_      :       Any          =       InstructBlipProcessor(
								    tokenizer=_A				, image_processor=_A				, qformer_tokenizer=_A      )

								UpperCAmelCase_      :       str          =       '''lower newer'''
								UpperCAmelCase_      :       List[str]          =       self.prepare_image_inputs()

								UpperCAmelCase_      :       Union[str, Any]          =       processor(text=_A				, images=_A      )

								self.assertListEqual(
								    list(inputs.keys()      )				, ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']				, )










'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


